title: Can visuospatial ability predict performance and learning curves on a robotic surgery simulator?
abstract: OBJECTIVE
  This study aimed to examine the correlation between visuospatial ability, measured with the Perceptual Ability Test (PAT), and da Vinci robot simulator performance on the da Vinci Skills Simulator.
  
  
  METHODS
  Twenty-five consenting medical students naive to both the PAT and the da Vinci robot completed the PAT and then performed a single simulation (Ring-walk 2) 10 consecutive times. Raw PAT scores were compared with composite simulator scores for all subjects. Participants were divided into those with high and low visuospatial ability based on whether they scored above or below (or equal) to the median on the PAT. We compared the mean composite simulator scores and the time to complete each exercise between the high and the low PAT performers.
  
  
  RESULTS
  The mean (SD) raw PAT score (out of 90) was 45.5 (18.3) (median, 43.0). The mean composite simulator score was 65.5 (24.1) (median, 72.2). The high (n = 12) and low (n = 13) PAT performers had a mean (SD) (median) simulator score of 79.1 (9.8) (80.3) and 53.0 (26.7) (65.9), respectively. On average, the high PAT performers scored 26.1 points (95% confidence interval, 9.2-43.0, P = 0.005), or 49.2%, higher on the simulation than the low PAT performers. The high PAT performers completed the exercise in 96.5 seconds (95% confidence interval, 26.0-167.0; P = 0.009), or 36.2%, faster than the low PAT performers.
  
  
  CONCLUSIONS
  Better visuospatial ability relates to improved performance on a robotic surgery simulator.
authors.affiliation: Division of Urogynecology, Hartford Hospital, Hartford, CT 06106, USA. bsuozzi@harthosp.org
authors.forename: Brent A
authors.initials: BA
authors.lastname: Suozzi
authors.forename: David M
authors.initials: DM
authors.lastname: O'Sullivan
authors.forename: Kyle T
authors.initials: KT
authors.lastname: Finnegan
authors.forename: Adam C
authors.initials: AC
authors.lastname: Steinberg
mesh_terms: D000328:Adult; D002983:Clinical Competence; D015331:Cohort Studies; D003198:Computer Simulation; D005260:Female; D006801:Humans; D059032:Learning Curve; D008297:Male; D008953:Models, Anatomic; D011237:Predictive Value of Tests; D012371:Robotics / Q000193:education*; D013028:Space Perception / Q000502:physiology*; D025321:Surgery, Computer-Assisted / Q000193:education*; D055815:Young Adult